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models.py
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models.py
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import numpy as np
import embedding
from keras.models import Sequential
from keras.layers import Dense, Embedding, LSTM, Merge, Reshape, Dropout, Convolution2D, MaxPooling2D, ZeroPadding2D, Flatten
def vis_lstm():
embedding_matrix = embedding.load()
embedding_model = Sequential()
embedding_model.add(Embedding(
embedding_matrix.shape[0],
embedding_matrix.shape[1],
weights = [embedding_matrix],
trainable = False))
image_model = Sequential()
image_model.add(Dense(
embedding_matrix.shape[1],
input_dim=4096,
activation='linear'))
image_model.add(Reshape((1,embedding_matrix.shape[1])))
main_model = Sequential()
main_model.add(Merge(
[image_model,embedding_model],
mode = 'concat',
concat_axis = 1))
main_model.add(LSTM(1001))
main_model.add(Dropout(0.5))
main_model.add(Dense(1001,activation='softmax'))
return main_model
def vis_lstm_2():
embedding_matrix = embedding.load()
embedding_model = Sequential()
embedding_model.add(Embedding(
embedding_matrix.shape[0],
embedding_matrix.shape[1],
weights = [embedding_matrix],
trainable = False))
image_model_1 = Sequential()
image_model_1.add(Dense(
embedding_matrix.shape[1],
input_dim=4096,
activation='linear'))
image_model_1.add(Reshape((1,embedding_matrix.shape[1])))
image_model_2 = Sequential()
image_model_2.add(Dense(
embedding_matrix.shape[1],
input_dim=4096,
activation='linear'))
image_model_2.add(Reshape((1,embedding_matrix.shape[1])))
main_model = Sequential()
main_model.add(Merge(
[image_model_1,embedding_model,image_model_2],
mode = 'concat',
concat_axis = 1))
main_model.add(LSTM(1001))
main_model.add(Dropout(0.5))
main_model.add(Dense(1001,activation='softmax'))
return main_model
def VGG_16(weights_path=None):
model = Sequential()
model.add(ZeroPadding2D((1,1),input_shape=(3,224,224)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides =(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides =(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides =(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides =(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides =(2,2)))
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1000, activation='softmax'))
if weights_path:
model.load_weights(weights_path)
return model